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techniques often ignore the joint statistics of weights and activations, producing sub-optimal CNN
performance at a given bit-rate, or consider their joint statistics during training only and do not facilitate
efficient compression of already trained CNN models. The proposed transform quantization framework
unifies quantization and dimensionality reduction (decorrelation) techniques in a single framework to
facilitate low bit-rate compression of CNNs and efficient inference in the transform domain. We first
introduce a theory of rate and distortion for CNN quantization, and pose optimum quantization as a rate-
distortion optimization problem. We then show that this problem can be solved using optimal bit-depth
allocation following decorrelation by the optimal End-to-end Learned Transform (ELT) we derive in this
paper. Read more here.
Fast optical flow extraction from compressed video. IEEE Trans Image Process, 2020. We propose the fast
optical flow extractor, a filtering method that recovers artifact-free optical flow fields from HEVC-
compressed video. To extract accurate optical flow fields, we form a regularized optimization problem that
considers the smoothness of the solution and the pixelwise confidence weights of an artifact-ridden HEVC
motion field. Solving such an optimization problem is slow, so we first convert the problem into a
confidence-weighted filtering task. By leveraging the already-available HEVC motion parameters, we
achieve a 100-fold speed-up in the running times compared to similar methods, while producing subpixel-
accurate flow estimates. The fast optical flow extractor is useful when video frames are already available in
coded formats. Our method is not specific to a coder, and works with motion fields from video coders such
as H.264/AVC and HEVC. Read more here.
Non-line-of-sight surface reconstruction using the directional light-cone transform. Proc IEEE CVPR,
2020. We propose a joint albedo–normal approach to non-line- of-sight (NLOS) surface reconstruction
using the directional light-cone transform (D-LCT). While current NLOS imaging methods reconstruct
either the albedo or surface normals of the hidden scene, the two quantities provide complementary
information of the scene, so an efficient method to estimate both simultaneously is desirable. We formulate
the recovery of the two quantities as a vector deconvolution problem, and solve it using the Cholesky–
Wiener decomposition. We show that surfaces fitted non-parametrically using our recovered normals are
more accurate than those produced with NLOS surface reconstruction methods recently proposed, and are
1,000× faster to compute than using inverse rendering. Read more here.